Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Enrichment: Journal of Multidisciplinary Research and Development
Fawwaz, Muhammad Jauhar
Unknown Affiliation
Humanities Economics, Econometrics & Finance Engineering Health Professions Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Development of Loan Default Prediction Models in Indonesia’s Multifinance Industry Fawwaz, Muhammad Jauhar; Zulkarnain, Zulkarnain
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 2 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i2.357

Abstract

This study develops a predictive model for loan default in Indonesia’s multifinance industry by implementing and comparing three machine learning methods: Logistic Regression, Support Vector Machine (SVM) with RBF kernel, and XGBoost. Using imbalanced datasets from three multifinance companies representing different portfolio characteristics vehicle, multipurpose, and electronics financing the research applies the SMOTE technique to address class imbalance and enhance model sensitivity. Results show that XGBoost outperforms both Logistic Regression and SVM in accuracy (0.9970), precision (0.9482), recall (0.9987), and AUC (0.9996), while also being the most computationally efficient. Feature importance analysis highlights late payment history, financial ratios, credit scores, and demographic variables as key predictors, with XGBoost capturing complex non-linear interactions. The study introduces a novel multi-layered framework for credit risk management, including scoring engines, early warning systems, and segment-based risk strategies. Segment analysis reveals higher default risks among younger, divorced, and less-educated borrowers, as well as for unsecured loans and high debt-to-income ratios. The model’s adaptability across varying institutional datasets demonstrates the need for company-specific calibration. Compared to previous single-model or single-company approaches, this research provides a comprehensive, scalable, and high-performing solution for predictive credit risk modeling in the Indonesian context. Simulation results suggest that the implementation of this framework could reduce NPF by up to 2.3 percentage points and enhance risk-adjusted returns by 3.8–4.2%, offering substantial practical value to multifinance companies.
Co-Authors Zulkarnain Zulkarnain